Modular Neural Architectures for Robotics
J.L. Buessler, Jean-Philippe Urban · Studies in fuzziness and soft computing · 2003
The learning of sensory-motor functions have motivated important research works that emphasize a major demand: the combination of multiple neural networks to implement complex functions. A review of a number of works presents some implementations in robotics, describing the purpose of the modular architecture, its structure, and the learning technique that was applied. The second part of the chapter presents an original approach to this problem of network training, proposed by our group. Based on a bi-directional architecture, multiple networks can be trained online with simple local learning rules, while the robotic systems interact with their environment. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.